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Quantification of Uncertainties in Snow Accumulation, Snowmelt, and Snow Disappearance Dates Mark S. Raleigh a Dissertation Subm

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Quantification of Uncertainties in Snow Accumulation, Snowmelt, and Snow Disappearance Dates Mark S. Raleigh a Dissertation Subm Quantification of uncertainties in snow accumulation, snowmelt, and snow disappearance dates Mark S. Raleigh A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Washington 2013 Reading Committee: Jessica D. Lundquist, Chair Janneke Hille Ris Lambers James Lutz Program Authorized to Offer Degree: Civil and Environmental Engineering ©Copyright 2013 Mark S. Raleigh University of Washington Abstract Quantification of uncertainties in snow accumulation, snowmelt, and snow disappearance dates Mark S. Raleigh Chair of the Supervisory Committee: Associate Professor Jessica D. Lundquist Civil and Environmental Engineering Seasonal mountain snowpack holds hydrologic and ecologic significance worldwide. However, observation networks in complex terrain are typically sparse and provide minimal information about prevailing conditions. Snow patterns and processes in this data sparse environment can be characterized with numerical models and satellite-based remote sensing, and thus it is essential to understand their reliability. This research quantifies model and remote sensing uncertainties in snow accumulation, snowmelt, and snow disappearance as revealed through comparisons with unique ground-based measurements. The relationship between snow accumulation uncertainty and model configuration is assessed through a controlled experiment at 154 snow pillow sites in the western United States. To simulate snow water equivalent (SWE), the National Weather Service SNOW-17 model is tested as (1) a traditional “forward” model based primarily on precipitation, (2) a reconstruction model based on total snowmelt before the snow disappearance date, and (3) a combination of (1) and (2). For peak SWE estimation, the reliability of the parent models was indistinguishable, while the combined model was most reliable. A sensitivity analysis demonstrated that the parent models had opposite sensitivities to temperature that tended to cancel in the combined model. Uncertainty in model forcing and parameters significantly controlled model accuracy. Uncertainty in remotely sensed snow cover and snow disappearance in forested areas is enhanced by canopy obstruction but has been ill-quantified due to the lack of sub-canopy observations. To better quantify this uncertainty, dense networks of near-surface temperature sensors were installed at four study areas (≤1 km 2) with varying forest cover in the Sierra Nevada, California. Snow presence at each sensor was detected during periods when temperature was damped, which resulted from snow cover insulation. This methodology was verified using time-lapse analysis and high resolution (15m) remote sensing, and then used to test daily 500 m canopy-adjusted MODIS snow cover data. Relative to the ground sensors, MODIS underestimated snow cover by 10-20% in meadows and 10-40% in forests, and showed snow disappearing 12 to 30 days too early in the forested sites. These errors were not detected with operational snow sensors, which have seen frequent use in MODIS validation studies. The link between model forcing and snow model uncertainty is assessed in two studies using measurements at well-instrumented weather stations in different snow climates. First, representation of snow surface temperature ( Ts) with temperature and humidity is examined because Ts tracks variations in the snowmelt energy balance. At all sites analyzed, the dew point temperature ( Td) represented Ts with lower bias than the dry and wet-bulb temperatures. The potential usefulness of this approximation is demonstrated in a case study where detection of model bias is achieved by comparing daily Td and modeled Ts. Second, the impact of forcing data availability and empirical data estimation is addressed to understand which types of data most impact physically-based snow modeling and need improved representation. An experiment is conducted at four well-instrumented sites with a series of hypothetical weather stations to determine which measurements (beyond temperature and precipitation) most impact snow model behavior. Radiative forcings had the largest impact on model behavior, but these are typically the least often measured. DEDICATION To Morgan, for her never-ending support, love, and encouragement. “Mountains should be climbed with as little effort as possible and without desire. The reality of your own nature should determine the speed. If you become restless, speed up. If you become winded, slow down. You climb the mountain in an equilibrium between restlessness and exhaustion. Then when you are no longer thinking ahead, each footstep isn’t just a means to an end but a unique event in itself. This leaf has jagged edges. This rock looks loose. From this place the snow is less visible, even though closer. These are things that you should notice anyway. To live only for some future goal is shallow. It’s the sides of the mountain that sustain life, not the top. Here’s where things grow.” -- Robert M. Pirsig, Zen and the Art of Motorcycle Maintenance: An Inquiry into Values ACKNOWLEDGEMENTS I am indebted to my academic advisor Dr. Jessica Lundquist for her guidance, enthusiasm, and patience while I discovered and developed my research abilities and style during my graduate education. She has been a refreshing example of how one can be both passionate and scientific about a personal interest. She has also shown me there are always new questions to be asked and always more to be learned, and this is usually done best by getting out of the office and into the field. Thank you for giving me the opportunity to research mountain hydrology with you. I would also like to thank my entire committee (Dr. Jessica Lundquist, Dr. Janneke Hille Ris Lambers, Dr. Jim Lutz, and Dr. Tom Ackerman) for dedicating time, energy, and interest in reviewing my research and for providing critical feedback from a diversity of perspectives. Thank you to the students and researchers in the Mountain Hydrology Research Group at the University of Washington (UW) for reading many paper drafts, offering ideas for improvements, and for bringing tasty treats to our weekly meetings. I would like to specifically thank my fellow graduate students who did field work in the Sierra Nevada with me, including Courtney Moore, Brian Henn, and Nic Wayand. You were good company on our long hikes, and were good sports about the summer heat, winter snow, and the ever present threat of marauding black bears. Thanks also to Susan Dickerson-Lange for providing on-demand feedback of figures and text. I would like to thank my past and ongoing collaborators outside of the UW Mountain Hydrology Group. I have had the pleasure of working with many individuals from other UW departments and from institutions outside of UW. There are far too many names to list here. My graduate education would have been impossible without support from multiple organizations, and I am grateful for their support. Funding for my final year was provided by the Hydro Research Foundation (Department of Energy). I was also supported by NASA Headquarters under the NASA Earth and Space Science Fellowship Program – Grant NNX09AO22H. My education was partially funded by the Joint Institute for the Study of the Atmosphere and Ocean under NOAA Cooperative Agreement NA10OAR4320148, Contribution No. 7436, in association with the NOAA Hydrometeorological Testbed Project. I would also like to acknowledge support from a UW Valle Scholarship, a UW Nece Fellowship, a United States Society on Dams Scholarship, a CH2M HILL EWB-USA Scholarship, an AWWA Scholarship (Ameron International) and a fellowship from the AWRA-WA section. I would like to acknowledge my family for setting the stage of this accomplishment. Thank you to my grandparents for settling in Colorado, where my fascination with mountains and snow began. Thanks to my parents for emphasizing the importance of quality education and for making many sacrifices along the way to ensure I had access to it. Thanks also to my siblings for sharing a love of snow and mountains. Thanks to the Forrey family for their curiosity in my work and for providing me with a home in the beautiful Pacific Northwest. Finally, I would like to thank my wife Morgan for patiently managing the inconveniences of me being in graduate school, including the late nights, the conference/field travel, and seemingly never ending stack of papers to read at home. I cannot thank her enough for her love and her confidence in me. At the time of writing, two of the four chapters have been published in peer-refereed journals, and I would like to acknowledge Water Resources Research (John Wiley and Sons) and Remote Sensing of Environment (Elsevier, Inc.) for granting permission to reproduce those papers in this dissertation. The formal acknowledgements are: Reprinted from Water Resources Research , 48 (1), Raleigh, M. S., and J. D. Lundquist, Comparing and combining SWE estimates from the SNOW-17 model using PRISM and SWE reconstruction, 1-16, 2012, with permission from Wiley. Reprinted from Remote Sensing of Environment , 128 , Raleigh, M. S., K. Rittger, C. E. Moore, B. Henn, J. A. Lutz, and J. D. Lundquist, Ground-based testing of MODIS fractional snow cover in subalpine meadows and forests of the Sierra Nevada, 44-57, 2013, with permission from Elsevier. TABLE OF CONTENTS Chapter 1 Introduction..................................................................................................................1 Chapter 2 Comparing and combining SWE estimates from the
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